Learning Certified Control Using Contraction Metric

Abstract

In this paper, we solve the problem of finding a certified control policy that drives a robot from any given initial state and under any bounded disturbance to the desired reference trajectory, with guarantees on the convergence or bounds on the tracking error. Such a controller is crucial in safe motion planning. We leverage the advanced theory in Control Contraction Metric and design a learning framework based on neural networks to co-synthesize the contraction metric and the controller for control-affine systems. We further provide methods to validate the convergence and bounded error guarantees. We demonstrate the performance of our method using a suite of challenging robotic models, including models with learned dynamics as neural networks. We compare our approach with leading methods using sum-of-squares programming, reinforcement learning, and model predictive control. Results show that our methods indeed can handle a broader class of systems with less tracking error and faster execution speed.

Publication
In Conference on Robot Learning 2020
Dawei Sun
Dawei Sun
(student at University of Illinois at Urbana-Champaign)
Susmit Jha
Susmit Jha
Technical Director, NuSCI

My research interests include artificial intelligence, formal methods, machine learning and dynamical systems.

Related